Towards Ethical Framework for Personal Care Robots: Review and Reflection
Bibliographic record
Abstract
In recent decades, robots have been used noticeably at various industries. Autonomous robots have been embedded in human lives especially in elderly and disabled lives. Elderly population is growing worldwide significantly; therefore there is an increased need of personal care robots to enhance mobility and to promote independence. A great number of aging and disabled hold appeals for using robots in daily routine tasks as well as for various healthcare matters. It is essential to follow a proper framework in ethics of robot design to fulfill individual needs, whilst considering potential harmful effects of robots. This paper primarily focuses on the existing issues in robot ethics including general ethics theories and ethics frameworks for robots. Consequentialism ethics will be recommended to be applied in robot ethics frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".